Microsatellite instability detection in cell-free dna

Abstract

Provided herein are methods for determining the microsatellite instability status of samples. In one aspect, the methods include quantifying a number of different repeat lengths present at each of a plurality of microsatellite loci from sequence information to generate a site score for each of the plurality of the microsatellite loci. The methods also include comparing the site score of a given microsatellite locus to a site specific trained threshold for the given microsatellite locus for each of the plurality of the microsatellite loci and calling the given microsatellite locus as being unstable when the site score of the given microsatellite locus exceeds the site specific trained threshold for the given microsatellite locus to generate a microsatellite instability score, which includes a number of unstable microsatellite loci from the plurality of the microsatellite loci.

1 . A method, comprising:
 (a) obtaining a sample comprising cell-free deoxyribonucleic acids (cfDNA) molecules from a human subject having cancer characterized by microsatellite instability;   (b) tagging the cfDNA molecules with a plurality of molecular barcodes;   (c) analyzing the molecular barcodes to generate sequence information comprising the number of different repeat lengths present at each of a plurality of microsatellite loci for the human subject;   (d) generating a plurality of likelihood scores comprising a score for each of the plurality of microsatellite loci based on: (i) allele frequency and (ii) noise amount in the sequence information;   (e) comparing each of the plurality of likelihood scores to a trained threshold corresponding to each of the plurality of microsatellite loci;   (f) identifying one or more of the plurality of microsatellite loci as unstable based on the comparison of the likelihood score of each of the plurality of microsatellite loci to the trained threshold corresponding each of the plurality of microsatellite loci   (g) generating an instability score based on the amount of identified unstable microsatellite loci in the plurality of microsatellite loci; and   (h) classifying the sample as being microsatellite unstable in the subject, if the instability score exceeds a population trained threshold for the plurality of microsatellite loci.   
     
     
         2 . The method of  claim 1 , wherein the likelihood score is a probabilistic log likelihood-based score. 
     
     
         3 . The method of  claim 1 , wherein the trained threshold corresponding to each of the plurality of microsatellite loci is the maximum value of a site score for a microsatellite locus to be classified as stable. 
     
     
         4 . The method of  claim 1 , wherein the likelihood score of a microsatellite loci comprises the probability of a somatic indel at that microsatellite locus. 
     
     
         5 . The method of  claim 1 , wherein the likelihood score comprises repeat lengths of observed sequencing reads covering a microsatellite locus. 
     
     
         6 . The method of  claim 1 , wherein the likelihood score comprises strand specific error parameter, random error parameter, or both. 
     
     
         7 . The method of  claim 6 , wherein the random error parameter comprises one or more of: a rate of read-level errors where a microsatellite length observed within a sequencing read is one repeat unit longer than an expected microsatellite length, and a rate of read-level errors where a microsatellite length observed within a sequencing read is one repeat unit shorter than an expected microsatellite length. 
     
     
         8 . The method of  claim 6 , wherein the strand specific error parameter comprises one or more of: a rate of strand-level errors where an expected microsatellite length of a sense strand is one repeat unit longer than an expected microsatellite length, a rate of strand-level errors where an expected microsatellite length of an antisense strand is one repeat unit longer than an expected microsatellite length, a rate of strand-level errors where an expected microsatellite length of a sense strand is one repeat unit shorter than an expected microsatellite length, and a rate of strand-level errors where an expected microsatellite length of an antisense strand is one repeat unit shorter than than an expected microsatellite length. 
     
     
         9 . The method of  claim 1 , comprising generating a trained threshold for each of the plurality of microsatellite loci by training on sequence information from a population of microsatellite loci in one or more training DNA samples, generating a population trained threshold by training on sequence information from a population of microsatellite loci in one or more training DNA samples, or both. 
     
     
         10 . The method of  claim 9 , wherein the one or more training DNA samples, comprises at least one of: (a) non-tumor cfDNA samples, and (b) DNA samples from one or more tumor types. 
     
     
         11 . The method of  claim 1 , wherein comprising classifying the instability score as high if the number of unstable microsatellite loci comprises about 0.1%, about 1%, about 2%, about 3%, about 4%, about 5%, about 10%, about 15%, about 20%, or about 25% of the plurality of the microsatellite loci. 
     
     
         13 . The method of  claim 1 , comprising: (i) administering at least one immunotherapy to the subject after classifying the sample as being microsatellite unstable, wherein the at least one immunotherapy comprises at least one of:
 an immune checkpoint molecule,   an antibody specific for an antigen selected from the group consisting of: PD-1, PD-2, PD-L1, PD-L2, CTLA-4, OX40, B7.1, B7He, LAG3, CD137, KIR, CCR5, CD27, CD40, and CD47,   proinflammatory cytokine selected from the group consisting of: IL-1B, IL-6, and TNF-α, and   activated T-cells, thereby treating the cancer in the human subject.   
     
     
         14 . The method of  claim 13 , wherein the wherein the immune checkpoint molecule comprises an inhibitory molecule that reduces a signal involved in the T cell response to antigen. 
     
     
         15 . The method of  claim 13 , wherein the immune checkpoint molecule comprises at least one of CTLA4, PD-1, PD-L1, PD-L2, CTLA4, CD80, CD86, lymphocyte activation gene 3 (LAG3), killer cell immunoglobulin like receptor (KIR), T cell membrane protein 3 (TIM3), galectin 9 (GAL9), or adenosine A2a receptor (A2aR). 
     
     
         16 . The method of  claim 13 , wherein the immune checkpoint molecule is a co-stimulatory molecule or a ligand of a co-stimulatory molecule comprising CD28, CD80, CD86, B7RP1, B7-H3, B7-H4, CD137L, OX40L, or CD70. 
     
     
         17 . The method of  claim 13 , wherein the antibody is pembrolizumab, nivolumab, ipilimumab, atezolizumab, avelumab, or durvalumab. 
     
     
         18 . The method of  claim 1 , wherein the human subject is afflicted with breast, colon or lung cancer.